project_final

library(readr)
library(dplyr)

## 
## Attaching package: 'dplyr'

## The following objects are masked from 'package:stats':
## 
##     filter, lag

## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union

library(tidyverse)

## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.1 ──

## ✓ ggplot2 3.3.5     ✓ purrr   0.3.4
## ✓ tibble  3.1.6     ✓ stringr 1.4.0
## ✓ tidyr   1.1.4     ✓ forcats 0.5.1

## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## x dplyr::filter() masks stats::filter()
## x dplyr::lag()    masks stats::lag()

library(ggplot2)
library(janitor)

## 
## Attaching package: 'janitor'

## The following objects are masked from 'package:stats':
## 
##     chisq.test, fisher.test

library(plotly)

## 
## Attaching package: 'plotly'

## The following object is masked from 'package:ggplot2':
## 
##     last_plot

## The following object is masked from 'package:stats':
## 
##     filter

## The following object is masked from 'package:graphics':
## 
##     layout

library(gganimate)
library(ggthemes)

Reading in:

whr <- read_csv("world-happiness-report.csv")

## Rows: 1949 Columns: 11
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr  (1): Country name
## dbl (10): year, Life Ladder, Log GDP per capita, Social support, Healthy lif...
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

TidyTuesday:

countries_plot <- whr %>% 
  clean_names() %>% 
  filter(country_name %in% c("Colombia","Philippines","China")) %>% 
  group_by(country_name) %>% 
  summarise(average_GDP = sum(log_gdp_per_capita)/n()) %>% 
  ggplot()+
  geom_col(aes(x=country_name,y=average_GDP, fill= country_name))+
  labs(title="Pia's, Adeline's and Marcela's Countries GDP's per Capita",
       x= " ",
       y= " ")+
  theme_minimal()+
  theme(legend.position = "none")

ggplotly(countries_plot)